There are many individuals who analyze financial data and financial instruments, such as equity and fixed-income securities. At least some of these individuals analyze such data in an attempt to predict future economic events. Such individuals may include, for example, security analysts and may be known as contributors or analysts, among others. The role of the security analyst is generally well-known and includes, among other things, the issuance of earnings or other financial estimates concerning future economic events and recommendations on whether investors should buy, sell, or hold financial instruments, such as equity securities. Security analyst estimates may include, but are not limited to, quarterly and annual earnings estimates for companies, whether or not they are traded on a public securities exchange.
At least some investors tend to rely on the earnings estimates and recommendations issued by security analysts. Usually more than one analyst follows a given equity security. Analysts often disagree on their earnings estimates and recommendations and, as a result, analysts' earnings estimates and recommendations may sometimes vary.
A number of financial information services providers (FISPs) gather and report analysts' earnings estimates and recommendations. At least some FISPs report the high, low, and mean earnings estimates, as well as mean recommendations for equity securities (as translated to a FISP's particular scale, for example, one to five). In addition, FISPs may also provide information on what the earnings estimates and recommendations were seven and thirty days prior to the most current consensus, as well as the differences between the consensus for a single equity security and that of the relevant industry. Moreover, for some clients, FISPs provide earnings estimates and recommendations on an analysts-by-analyst basis. An advantage of the availability of analyst-level estimates and recommendations is that a client can view the components of the mean estimate or recommendation by analyst. Various drawbacks exist, however, with these approaches and other known techniques.
For example, prior approaches include a software program that displays all current estimates. For a particular fiscal period for a particular security the software provides the ability to simply “include” or “exclude” each estimate (recommendation) from the mean. This is problematic for several reasons. First, commercially available databases of estimates and recommendations contain “current” data on thousands of stocks. Each stock may have estimates from 1 to 70 or more analysts. Each analyst may provide estimates for 1 to many periods. The data may be updated throughout the day. Manually dealing with this volume of information can be time consuming.
A second drawback is that with current techniques, if someone were inclined to determine which estimates (recommendations) should get more weight, and which estimates should get less or no weight, the sheer volume of analysts (over 3,000 for U.S. stocks alone) makes it extremely difficult to know which analysts provide more useful information than others. Current techniques lack sufficient ability to intelligently measure historical analyst performance and beneficially use such measurements.
A third drawback is that it while it is possible to imagine various weighting systems or algorithms, it is difficult to effectively implement or test them. Current systems do not provide the ability to effectively devise new estimate (recommendation) weighting algorithms; nor do they provide the ability to easily test their (hypothetical) historical performance.
A fourth drawback with current techniques is that there are limited or no tools for effectively viewing historical estimates and recommendations as time-series graphs or for overlaying this information over a graph of prices for the securities to understand the relationship between changes in estimates (recommendations) to changes in securities prices. These and other drawbacks exist with existing systems